Cohort profile: the TrueNTH Global Registry - an international registry to monitor and improve localised prostate cancer health outcomes
Bibliographic record
Abstract
PURPOSE: Globally, prostate cancer treatment and outcomes for men vary according to where they live, their race and the care they receive. The TrueNTH Global Registry project was established as an international registry monitoring care provided to men with localised prostate cancer (CaP). PARTICIPANTS: Sites with existing CaP databases in Movember fundraising countries were invited to participate in the international registry. In total, 25 Local Data Centres (LDCs) representing 113 participating sites across 13 countries have nominated to contribute to the project. It will collect a dataset based on the International Consortium for Health Outcome Measures (ICHOM) standardised dataset for localised CaP. FINDINGS TO DATE: A governance strategy has been developed to oversee registry operation, including transmission of reversibly anonymised data. LDCs are represented on the Project Steering Committee, reporting to an Executive Committee. A Project Coordination Centre and Data Coordination Centre (DCC) have been established. A project was undertaken to compare existing datasets, understand capacity at project commencement (baseline) to collect the ICHOM dataset and assist in determining the final data dictionary. 21/25 LDCs provided data dictionaries for review. Some ICHOM data fields were well collected (diagnosis, treatment start dates) and others poorly collected (complications, comorbidities). 17/94 (18%) ICHOM data fields were relegated to non-mandatory fields due to poor capture by most existing registries. Participating sites will transmit data through a web interface biannually to the DCC. FUTURE PLANS: Recruitment to the TrueNTH Global Registry-PCOR project will commence in late 2017 with sites progressively contributing reversibly anonymised data following ethical review in local regions. Researchers will have capacity to source deidentified data after the establishment phase. Quality indicators are to be established through a modified Delphi approach in later 2017, and it is anticipated that reports on performance against quality indicators will be provided to LDCs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".